NIMH Data Archive - Data

Genomics

Neuroimaging

Phenotype¹

New Trial
Clinical Trial

¹ Numbers reported are subjects by age

New Project
Grant/Project Number

Format should be in the following format: Activity Code, Institute Abbreviation, and Serial Number. Grant Type, Support Year, and Suffix should be excluded. For example, grant 1R01MH123456-01A1 should be entered R01MH123456

Collection - Use Existing Experiment

To associate an experiment to the current collection, just select an axperiment from the table below then click the associate experiment button to persist your changes (saving the collection is not required). Note that once an experiment has been associated to two or more collections, the experiment will not longer be editable.

The table search feature is case insensitive and targets the experiment id, experiment name and experiment type columns. The experiment id is searched only when the search term entered is a number, and filtered using a startsWith comparison. When the search term is not numeric the experiment name is used to filter the results.

Select	Experiment Id	Experiment Name	Experiment Type	Created On

24	HI-NGS_R1	Omics	02/16/2011
475	MB1-10 (CHOP)	Omics	06/07/2016
490	Discovery and CRISPR validation of genetic factors associated with antipsychotic-induced weight gain and cardiometabolic risk	Omics	07/07/2016
501	PharmacoBOLD Resting State	fMRI	07/27/2016
506	PVPREF	Omics	08/05/2016
509	ABC-CT Resting v2	EEG	08/18/2016
13	Comparison of FI expression in Autistic and Neurotypical Homo Sapiens	Omics	12/28/2010
18	AGRE/Broad Affymetrix 5.0 Genotype Experiment	Omics	01/06/2011
22	Stitching PCR Sequencing	Omics	02/14/2011
26	ASD_Methylation	Omics	03/01/2011
29	Microarray family 03 (father, mother, sibling)	Omics	03/24/2011
37	Standard paired-end sequencing of BCRs	Omics	04/19/2011
38	Illumina Mate-Pair BCR sequencing	Omics	04/19/2011
39	Custom Jumping Libraries	Omics	04/19/2011
40	Custom CapBP	Omics	04/19/2011
41	Immunofluorescence	Omics	05/11/2011
43	Autism brain sample genotyping, Illumina	Omics	05/16/2011
47	ARRA Autism Sequencing Collaboration at Baylor. SOLiD 4 System	Omics	08/01/2011
53	AGRE Omni1-quad	Omics	10/11/2011
59	AGP genotyping	Omics	04/03/2012
60	Ultradeep 454 sequencing of synaptic genes from postmortem cerebella of individuals with ASD and neurotypical controls	Omics	06/23/2012
63	Microemulsion PCR and Targeted Resequencing for Variant Detection in ASD	Omics	07/20/2012
76	Whole Genome Sequencing in Autism Families	Omics	01/03/2013
519	Resting	fMRI	11/08/2016
90	Genotyped IAN Samples	Omics	07/09/2013
91	NJLAGS Axiom Genotyping Array	Omics	07/16/2013
93	AGP genotyping (CNV)	Omics	09/06/2013
106	Longitudinal Sleep Study. H20 200. Channel set 2	EEG	11/07/2013
107	Longitudinal Sleep Study. H20 200. Channel set 3	EEG	11/07/2013
108	Longitudinal Sleep Study. AURA 200	EEG	11/07/2013
105	Longitudinal Sleep Study. H20 200. Channel set 1	EEG	11/07/2013
109	Longitudinal Sleep Study. AURA 400	EEG	11/07/2013
116	Gene Expression Analysis WG-6	Omics	01/07/2014
131	Jeste Lab UCLA ACEii: Charlie Brown and Sesame Street - Project 1	Eye Tracking	02/27/2014
132	Jeste Lab UCLA ACEii: Animacy - Project 1	Eye Tracking	02/27/2014
133	Jeste Lab UCLA ACEii: Mom Stranger - Project 2	Eye Tracking	02/27/2014
134	Jeste Lab UCLA ACEii: Face Emotion - Project 3	Eye Tracking	02/27/2014
145	AGRE/FMR1_Illumina.JHU	Omics	04/14/2014
146	AGRE/MECP2_Sanger.JHU	Omics	04/14/2014
147	AGRE/MECP2_Junior.JHU	Omics	04/14/2014
151	Candidate Gene Identification in familial Autism	Omics	06/09/2014
152	NJLAGS Whole Genome Sequencing	Omics	07/01/2014
154	Math Autism Study - Vinod Menon	fMRI	07/15/2014
155	Resting	fMRI	07/25/2014
156	Speech	fMRI	07/25/2014
159	Emotion	fMRI	07/25/2014
160	syllable contrast	EEG	07/29/2014
167	School-age naturalistic stimuli	Eye Tracking	09/19/2014
44	AGRE/Broad Affymetrix 5.0 Genotype Experiment	Omics	06/27/2011
45	Exome Sequencing of 20 Sporadic Cases of Autism Spectrum Disorder	Omics	07/15/2011

Collection - Add Experiment

Add Supporting Documentation

Funding Source:
URL:

To add an existing Data Structure, enter its title in the search bar. If you need to request changes, select the indicator "No, it requires changes to meet research needs" after selecting the Structure, and upload the file with the request changes specific to the selected Data Structure. Your file should follow the Request Changes Procedure. If the Data Structure does not exist, select "Request New Data Structure" and upload the appropriate zip file.

Use/Modify Existing Data Structure

Request New Data Structure

Targeted Enrollment:

Initial Submission Date:

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Data Structures:

Submit

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The Data Expected list for this Collection shows some raw data as missing. Contact the NDA Help Desk with any questions.

Please confirm that you will not be enrolling any more subjects and that all raw data has been collected and submitted.

Collection Updated

Your Collection is now in Data Analysis phase and exempt from biannual submissions. Analyzed data is still expected prior to publication or no later than the project end date.

[CMS] Error

[CMS]

Unable to change collection phase where targeted enrollment is less than 90%

You have requested to move the sharing dates for the following assessments:

Data Expected Item	Original Sharing Date	New Sharing Date

Please provide a reason for this change, which will be sent to the Program Officers listed within this collection:

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Computational, Neural, and Behavioral Studies of Competition-Dependent Learning #2521

General
Experiments (14)
Shared Data
Publications (44)
Data Expected (4)
Associated Studies (12)

Collection Title	Collection Investigators	Collection Description
Collection Title:	Computational, Neural, and Behavioral Studies of Competition-Dependent Learning
Collection Investigators:	Kenneth Norman
Collection Description:	Our overarching goal is to understanding how stored memories change as a function of experience. The pro-posed work builds on prior research showing a U-shaped relationship between memory activation and learn-ing, whereby strong activation leads to synaptic strengthening, moderate activation leads to synaptic weaken-ing, and no activation leads to no change in synaptic strength. The present grant focuses on the implications of this U-shaped relationship for representational change Learning is not just about making memories stronger or weaker it can also decrease neural overlap between memories differentiation or increase neural overlap integration. These neural changes can have profound effects on memory retrieval Decreased overlap can reduce interference, at the cost of preventing generalization. Our specific goal is to construct and test a com-putational model of representational change and how it is shaped by competitive neural dynamics. When im-plemented in neural networks that are capable of self-organizing internal representations, our theory makes clear, novel predictions about when differentiation and integration will occur Differentiation of memories A and B will occur when i B is moderately activated while processing A, causing weakening of connections between B and A, and ii B is reactivated later, allowing it to acquire new features that do not overlap with A by con-trast, integration will occur if B is strongly activated during A, causing strengthening of connections between Band A. Aim 1 will use neural network simulations to address vexing puzzles in the literature and to generate novel empirical predictions. Aim 2 will test these predictions using behavioral and fMRI experiments focused on learning of new associations in the hippocampus, with a particular emphasis on testing the models predic-tions about how competitive dynamics relate to representational change. Aim 3 will test the models predictions regarding cortical plasticity, using a novel sketching task that induces competition between representations of familiar objects. Representational change will be assessed behaviorally in terms of how sketches and object recognition change over learning and neurally using fMRI of visual cortex a deep neural network model of the ventral stream will be used to measure changes in the features of sketches. In summary The proposed studies use multiple innovative approaches fMRI pattern analysis, neural network modeling, free-form object sketching, and computer vision to address the fundamental question of when experience causes neural repre-sentations to differentiate or integrate, thereby advancing our basic understanding of neuroplasticity. Improving our understanding of neural differentiation could have transformative implications for treating cognitive deficits in a wide range of clinical conditions, including stroke, dyslexia, and dementia. In all of these conditions, cogni-tive deficits can arise from insufficient separation of representations. This research may lead to better ways of re-differentiating these representations and through this ameliorating the associated cognitive deficits.
Data Repository:	NIMH Data Archive
Permission Group:
Collection Creation Date:	09/29/2016
Collection Phase:	Funding Completed
Collection Sub-Phase:	Close Out
Blinded Clinical Trial:	No
Total Funded Amount:	$1,919,481.00
Subjects Shared:	410
Collection DOI:	10.15154/0r97-k395

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Funding Sources:

Funding Source Name	Funding Source URL
NIH - Extramural	None

Supporting Documentation:

Grant Information:

Project Number	Title	Start Date	Original End Date	End Date	Planned Enrollment	Actual Enrollment	Organization	Funds Obligated
R01MH069456-11	Computational, Neural, and Behavioral Studies of Competition-Dependent Learning	09/19/2016	06/30/2021	06/30/2021	986	2053	PRINCETON UNIVERSITY	$1,919,481.00

Clinical Trials:

helpcenter.collection.general-tab

Collection - General Tab

Fields available for edit on the top portion of the page include:

Collection Title
Investigators
Collection Description
Collection Phase
Funding Source
Clinical Trials

Collection Phase: The current status of a research project submitting data to an NDA Collection, based on the timing of the award and/or the data that have been submitted.

Pre-Enrollment: The default entry made when the NDA Collection is created.
Enrolling: Data have been submitted to the NDA Collection or the NDA Data Expected initial submission date has been reached for at least one data structure category in the NDA Collection.
Data Analysis: Subject level data collection for the research project is completed and has been submitted to the NDA Collection. The NDA Collection owner or the NDA Help Desk may set this phase when they’ve confirmed data submission is complete and submitted subject counts match at least 90% of the target enrollment numbers in the NDA Data Expected. Data submission reminders will be turned off for the NDA Collection.
Funding Completed: The NIH grant award (or awards) associated with the NDA Collection has reached its end date. NDA Collections in Funding Completed phase are assigned a subphase to indicate the status of data submission.
- The Data Expected Subphase indicates that NDA expects more data will be submitted
- The Closeout Subphase indicates the data submission is complete.
- The Sharing Not Met Subphase indicates that data submission was not completed as expected.

Blinded Clinical Trial Status:

This status is set by a Collection Owner and indicates the research project is a double blinded clinical trial. When selected, the public view of Data Expected will show the Data Expected items and the Submission Dates, but the targeted enrollment and subjects submitted counts will not be displayed.
Targeted enrollment and subjects submitted counts are visible only to NDA Administrators and to the NDA Collection or as the NDA Collection Owner.
When an NDA Collection that is flagged Blinded Clinical Trial reaches the maximum data sharing date for that Data Repository (see https://nda.nih.gov/nda/sharing-regimen.html), the embargo on Data Expected information is released.

Funding Source

The organization(s) responsible for providing the funding is listed here.

Supporting Documentation

Users with Submission privileges, as well as Collection Owners, Program Officers, and those with Administrator privileges, may upload and attach supporting documentation. By default, supporting documentation is shared to the general public, however, the option is also available to limit this information to qualified researchers only.

Grant Information

Identifiable details are displayed about the Project of which the Collection was derived from. You may click in the Project Number to view a full report of the Project captured by the NIH.

Clinical Trials

Any data that is collected to support or further the research of clinical studies will be available here. Collection Owners and those with Administrator privileges may add new clinical trials.

Frequently Asked Questions

How does the NIMH Data Archive (NDA) determine which Permission Group data are submitted into?

During Collection creation, NDA staff determine the appropriate Permission Group based on the type of data to be submitted, the type of access that will be available to data access users, and the information provided by the Program Officer during grant award.
How do I know when a NDA Collection has been created?

When a Collection is created by NDA staff, an email notification will automatically be sent to the PI(s) of the grant(s) associated with the Collection to notify them.
Is a single grant number ever associated with more than one Collection?

The NDA system does not allow for a single grant to be associated with more than one Collection; therefore, a single grant will not be listed in the Grant Information section of a Collection for more than one Collection.
Why is there sometimes more than one grant included in a Collection?

In general, each Collection is associated with only one grant; however, multiple grants may be associated if the grant has multiple competing segments for the same grant number or if multiple different grants are all working on the same project and it makes sense to hold the data in one Collection (e.g., Cooperative Agreements).

Glossary

Administrator Privilege

A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users.
Collection Owner

Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.
Collection Phase
The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different. Collection users with Administrative Privileges are encouraged to edit the Collection Phase. The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page. This field is sortable alphabetically in ascending or descending order. Collection Phase options include:
- Pre-Enrollment: A grant/project has started, but has not yet enrolled subjects.
- Enrolling: A grant/project has begun enrolling subjects. Data submission is likely ongoing at this point.
- Data Analysis: A grant/project has completed enrolling subjects and has completed all data submissions.
- Funding Completed: A grant/project has reached the project end date.
Collection Title

An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.
Grant

Provides the grant number(s) for the grant(s) associated with the Collection. The field is a hyperlink so clicking on the Grant number will direct the user to the grant information in the NIH Research Portfolio Online Reporting Tools (RePORT) page.
Supporting Documentation

Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.
NIH Research Initiative

NDA Collections may be organized by scientific similarity into NIH Research Initiatives, to facilitate query tool user experience. NIH Research Initiatives map to one or multiple Funding Opportunity Announcements.
Permission Group

Access to shared record-level data in NDA is provisioned at the level of a Permission Group. NDA Permission Groups consist of one or multiple NDA Collections that contain data with the same subject consents.
Planned Enrollment

Number of human subject participants to be enrolled in an NIH-funded clinical research study. The data is provided in competing applications and annual progress reports.
Actual Enrollment

Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
NDA Collection

A virtual container and organization structure for data and associated documentation from one grant or one large project/consortium. It contains tools for tracking data submission and allows investigators to define a wide array of other elements that provide context for the data, including all general information regarding the data and source project, experimental parameters used to collect any event-based data contained in the Collection, methods, and other supporting documentation. They also allow investigators to link underlying data to an NDA Study, defining populations and subpopulations specific to research aims.
Data Use Limitations

Data Use Limitations (DULs) describe the appropriate secondary use of a dataset and are based on the original informed consent of a research participant. NDA only accepts consent-based data use limitations defined by the NIH Office of Science Policy.
Total Subjects Shared

The total number of unique subjects for whom data have been shared and are available for users with permission to access data.

Contact NDA Help Desk

ID	Name	Created Date	Status	Type
657	Violation Differentiation	05/05/2017	Approved	fMRI
919	Neural overlap in item representations across episodes impairs context memory	05/11/2018	Approved	fMRI
1027	Human hippocampal replay during rest prioritizes weakly-learned information and predicts memory performance	08/02/2018	Approved	fMRI
1029	Reductions in retrieval competition predict the benefit of repeated testing	08/10/2018	Approved	EEG
1066	Associative prediction of visual shape in the hippocampus	09/13/2018	Approved	fMRI
1156	Sketch Loop	01/08/2019	Approved	fMRI
1281	Stat Learning	07/05/2019	Approved	fMRI
1411	Hippocampal-neocortical interactions sharpen over time for predictive actions.	10/03/2019	Approved	fMRI
1520	Content-based dissociation of hippocampal involvement in prediction	01/31/2020	Approved	fMRI
1542	Statistical Learning	03/29/2020	Approved	fMRI
1649	The Role of Sleep in Modulating Representational Change	01/06/2021	Approved	fMRI
1700	Sculpting New Visual Concepts into the Human Brain	02/14/2021	Approved	fMRI
2094	Emotional Think/No-Think fMRI Study	07/15/2022	Approved	fMRI
2204	Generalization & exception learning in word-image association task	02/20/2023	Approved	fMRI

helpcenter.collection.experiments-tab

Collection - Experiments

The number of Experiments included is displayed in parentheses next to the tab name. You may download all experiments associated with the Collection via the Download button. You may view individual experiments by clicking the Experiment Name and add them to the Filter Cart via the Add to Cart button.

Collection Owners, Program Officers, and users with Submission or Administrative Privileges for the Collection may create or edit an Experiment.

Please note: The creation of an NDA Experiment does not necessarily mean that data collected, according to the defined Experiment, has been submitted or shared.

Frequently Asked Questions

Can an Experiment be associated with more than one Collection?
Yes -see the “Copy” button in the bottom left when viewing an experiment. There are two actions that can be performed via this button:
1. Copy the experiment with intent for modifications.
2. Associate the experiment to the collection. No modifications can be made to the experiment.

Glossary

Experiment Status

An Experiment must be Approved before data using the associated Experiment_ID may be uploaded.
Experiment ID

The ID number automatically generated by NDA which must be included in the appropriate file when uploading data to link the Experiment Definition to the subject record.

Contact NDA Help Desk

Shared Data:

Title	Type	Number of Subjects
EEG Subject Files	Imaging	40
Image	Imaging	370

helpcenter.collection.shared-data-tab

Collection - Shared Data

This tab provides a quick overview of the Data Structure title, Data Type, and Number of Subjects that are currently Shared for the Collection. The information presented in this tab is automatically generated by NDA and cannot be edited. If no information is visible on this tab, this would indicate the Collection does not have shared data or the data is private.

The shared data is available to other researchers who have permission to access data in the Collection's designated Permission Group(s). Use the Download button to get all shared data from the Collection to the Filter Cart.

Frequently Asked Questions

How will I know if another researcher uses data that I shared through the NIMH Data Archive (NDA)?

To see what data your project have submitted are being used by a study, simply go the Associated Studies tab of your collection. Alternatively, you may review an NDA Study Attribution Report available on the General tab.
Can I get a supplement to share data from a completed research project?

Often it becomes more difficult to organize and format data electronically after the project has been completed and the information needed to create a GUID may not be available; however, you may still contact a program staff member at the appropriate funding institution for more information.
Can I get a supplement to share data from a research project that is still ongoing?

Unlike completed projects where researchers may not have the information needed to create a GUID and/or where the effort needed to organize and format data becomes prohibitive, ongoing projects have more of an opportunity to overcome these challenges. Please contact a program staff member at the appropriate funding institution for more information.

Glossary

Data Structure

A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points. Data structures that have been defined in the NDA Data Dictionary are available at https://nda.nih.gov/general-query.html?q=query=data-structure
Data Type

A grouping of data by similar characteristics such as Clinical Assessments, Omics, or Neurosignal data.
Shared

The term 'Shared' generally means available to others; however, there are some slightly different meanings based on what is Shared. A Shared NDA Study is viewable and searchable publicly regardless of the user's role or whether the user has an NDA account. A Shared NDA Study does not necessarily mean that data used in the NDA Study have been shared as this is independently determined. Data are shared according the schedule defined in a Collection's Data Expected Tab and/or in accordance with data sharing expectations in the NDA Data Sharing Terms and Conditions. Additionally, Supporting Documentation uploaded to a Collection may be shared independent of whether data are shared.

Contact NDA Help Desk

Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:

Publications

Publications relevant to NDA data are listed below. Most displayed publications have been associated with the grant within Pubmed. Use the "+ New Publication" button to add new publications. Publications relevant/not relevant to data expected are categorized. Relevant publications are then linked to the underlying data by selecting the Create Study link. Study provides the ability to define cohorts, assign subjects, define outcome measures and lists the study type, data analysis and results. Analyzed data and results are expected in this way.

PubMed ID	Study	Title	Journal	Authors	Date	Status
42265352	Create Study	Human learning of noninvasive brain-computer interfaces via manifold geometry.	Nature neuroscience	Busch, Erica L; Fincke, E Chandra; Lajoie, Guillaume; Krishnaswamy, Smita; Turk-Browne, Nicholas B	June 9, 2026	Not Determined
41676664	Create Study	Learning of visual sequences by neurons in the human hippocampus.	bioRxiv : the preprint server for biology	John, Tibin; Zhou, Yajun; Aljishi, Ayman; Rieck, Bastian; Turk-Browne, Nicholas B; Damisah, Eyiyemisi C	February 3, 2026	Not Determined
41394708	Create Study	Synthesizing images to map neural networks to the human brain.	bioRxiv : the preprint server for biology	Peng, Kailong; Norman, Kenneth A; Turk-Browne, Nicholas B; Wammes, Jeffrey D	November 25, 2025	Not Determined
40737528	Create Study	The Role of Rapid Eye Movement Sleep in Neural Differentiation of Memories in the Hippocampus.	Journal of cognitive neuroscience	McDevitt, Elizabeth A; Kim, Ghootae; Turk-Browne, Nicholas B; Norman, Kenneth A	January 1, 2026	Relevant
39625982	Create Study	Sculpting new visual categories into the human brain.	Proceedings of the National Academy of Sciences of the United States of America	Iordan, Coraline Rinn; Ritvo, Victoria J H; Norman, Kenneth A; Turk-Browne, Nicholas B; Cohen, Jonathan D	December 10, 2024	Not Determined
39428880	Create Study	Inducing representational change in the hippocampus through real-time neurofeedback.	Philosophical transactions of the Royal Society of London. Series B, Biological sciences	Peng, Kailong; Wammes, Jeffrey D; Nguyen, Alex; Iordan, Coraline Rinn; Norman, Kenneth A; Turk-Browne, Nicholas B	December 2, 2024	Not Determined
39319791	Create Study	A neural network model of differentiation and integration of competing memories.	eLife	Ritvo, Victoria J H; Nguyen, Alex; Turk-Browne, Nicholas B; Norman, Kenneth A	September 25, 2024	Not Determined
38435074	Create Study	How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?	Advances in neural information processing systems	Zhuang, Chengxu; Xiang, Violet; Bai, Yoon; Jia, Xiaoxuan; Turk-Browne, Nicholas; Norman, Kenneth; DiCarlo, James J; Yamins, Daniel L K	January 1, 2022	Not Determined
38116686	Create Study	Statistical learning in epilepsy: Behavioral and anatomical mechanisms in the human brain.	Epilepsia	Aljishi, Ayman; Sherman, Brynn E; Huberdeau, David M; Obaid, Sami; Khan, Kamren; Lamsam, Layton; Zibly, Zion; Sivaraju, Adithya; Turk-Browne, Nicholas B; Damisah, Eyiyemisi C	March 1, 2024	Not Determined
38106228	Create Study	INDUCING REPRESENTATIONAL CHANGE IN THE HIPPOCAMPUS THROUGH REAL-TIME NEUROFEEDBACK.	bioRxiv : the preprint server for biology	Peng, Kailong; Wammes, Jeffrey D; Nguyen, Alex; Cătălin Iordan, Marius; Norman, Kenneth A; Turk-Browne, Nicholas B	December 4, 2023	Not Determined
37390333	Create Study	Multiple Memory Subsystems: Reconsidering Memory in the Mind and Brain.	Perspectives on psychological science : a journal of the Association for Psychological Science	Sherman, Brynn E; Turk-Browne, Nicholas B; Goldfarb, Elizabeth V	January 1, 2024	Not Determined
37066178	Create Study	Differentiation and Integration of Competing Memories: A Neural Network Model.	bioRxiv : the preprint server for biology	Ritvo, Victoria J H; Nguyen, Alex; Turk-Browne, Nicholas B; Norman, Kenneth A	June 25, 2024	Not Determined
36344264	Create Study	Temporal Dynamics of Competition between Statistical Learning and Episodic Memory in Intracranial Recordings of Human Visual Cortex.	The Journal of neuroscience : the official journal of the Society for Neuroscience	Sherman, Brynn E; Graves, Kathryn N; Huberdeau, David M; Quraishi, Imran H; Damisah, Eyiyemisi C; Turk-Browne, Nicholas B	November 30, 2022	Not Determined
36279437	Create Study	A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation.	Proceedings of the National Academy of Sciences of the United States of America	Singh, Dhairyya; Norman, Kenneth A; Schapiro, Anna C	November 1, 2022	Not Determined
35961387	Create Study	Remembering the pattern: A longitudinal case study on statistical learning in spatial navigation and memory consolidation.	Neuropsychologia	Graves, Kathryn N; Sherman, Brynn E; Huberdeau, David; Damisah, Eyiyemisi; Quraishi, Imran H; Turk-Browne, Nicholas B	September 9, 2022	Not Determined
34989336	Create Study	Increasing stimulus similarity drives nonmonotonic representational change in hippocampus.	eLife	Wammes, Jeffrey; Norman, Kenneth A; Turk-Browne, Nicholas	January 6, 2022	Not Determined
33608265	Create Study	Learning hierarchical sequence representations across human cortex and hippocampus.	Science advances	Henin, Simon; Turk-Browne, Nicholas B; Friedman, Daniel; Liu, Anli; Dugan, Patricia; Flinker, Adeen; Doyle, Werner; Devinsky, Orrin; Melloni, Lucia	February 1, 2021	Not Determined
33475417	Create Study	Visuomotor associations facilitate movement preparation.	Journal of experimental psychology. Human perception and performance	Huberdeau, David M; Turk-Browne, Nicholas B	March 1, 2021	Not Determined
33431673	Create Study	Unsupervised neural network models of the ventral visual stream.	Proceedings of the National Academy of Sciences of the United States of America	Zhuang, Chengxu; Yan, Siming; Nayebi, Aran; Schrimpf, Martin; Frank, Michael C; DiCarlo, James J; Yamins, Daniel L K	January 19, 2021	Not Determined
33271266	Create Study	Emergence and organization of adult brain function throughout child development.	NeuroImage	Yates, Tristan S; Ellis, Cameron T; Turk-Browne, Nicholas B	February 1, 2021	Not Determined
33270655	Create Study	Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain.	PLoS computational biology	Kumar, Sreejan; Ellis, Cameron T; O'Connell, Thomas P; Chun, Marvin M; Turk-Browne, Nicholas B	December 1, 2020	Not Determined
32859755	Create Study	Statistical prediction of the future impairs episodic encoding of the present.	Proceedings of the National Academy of Sciences of the United States of America	Sherman, Brynn E; Turk-Browne, Nicholas B	September 15, 2020	Not Determined
32853531	Create Study	Finding the Pattern: On-Line Extraction of Spatial Structure During Virtual Navigation.	Psychological science	Graves, Kathryn N; Antony, James W; Turk-Browne, Nicholas B	September 2020	Not Determined
32258249	Create Study	The prevalence and importance of statistical learning in human cognition and behavior.	Current opinion in behavioral sciences	Sherman, Brynn E; Graves, Kathryn N; Turk-Browne, Nicholas B	April 2020	Not Determined
31871278	Study (647)	Relating Visual Production and Recognition of Objects in Human Visual Cortex.	The Journal of neuroscience : the official journal of the Society for Neuroscience	Fan JE, Wammes JD, Gunn JB, Yamins DLK, Norman KA, Turk-Browne NB	February 2020	Not Determined

helpcenter.collection.publications-tab

Collection - Publications

The number of Publications is displayed in parentheses next to the tab name. Clicking on any of the Publication Titles will open the Publication in a new internet browsing tab.

Collection Owners, Program Officers, and users with Submission or Administrative Privileges for the Collection may mark a publication as either Relevant or Not Relevant in the Status column.

Frequently Asked Questions

How can I determine if a publication is relevant?

Publications are considered relevant to a collection when the data shared is directly related to the project or collection.
Where does the NDA get the publications?

PubMed, an online library containing journals, articles, and medical research. Sponsored by NiH and National Library of Medicine (NLM).

Glossary

Create Study

A link to the Create an NDA Study page that can be clicked to start creating an NDA Study with information such as the title, journal and authors automatically populated.
Not Determined Publication

Indicates that the publication has not yet been reviewed and/or marked as Relevant or Not Relevant so it has not been determined whether an NDA Study is expected.
Not Relevant Publication

A publication that is not based on data related to the aims of the grant/project associated with the Collection or not based on any data such as a review article and, therefore, an NDA Study is not expected to be created.
PubMed

PubMed provides citation information for biomedical and life sciences publications and is managed by the U.S. National Institutes of Health's National Library of Medicine.
PubMed ID

The PUBMed ID is the unique ID number for the publication as recorded in the PubMed database.
Relevant Publication

A publication that is based on data related to the aims of the grant/project associated with the Collection and, therefore, an NDA Study is expected to be created.

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Data Expected List: Mandatory Data Structures

These data structures are mandatory for your NDA Collection. Please update the Targeted Enrollment number to accurately represent the number of subjects you expect to submit for the entire study.

For NIMH HIV-related research that involves human research participants: Select the dictionary or dictionaries most appropriate for your research. If your research does not require all three data dictionaries, just ignore the ones you do not need. There is no need to delete extra data dictionaries from your NDA Collection. You can adjust the Targeted Enrollment column in the Data Expected tab to “0” for those unnecessary data dictionaries. At least one of the three data dictionaries must have a non-zero value.

Data Expected	Targeted Enrollment	Initial Submission	Subjects Submitted	Initial Share	Subjects Shared	Status
Research Subject and Pedigree	320	07/15/2017	0	11/15/2017	0	Approved

To create your project's Data Expected list, use the "+New Data Expected" to add or request existing structures and to request new Data Structures that are not in the NDA Data Dictionary.

If the Structure you need already exists, locate it and specify your dates and enrollment when adding it to your Data Expected list. If you require changes to the Structure you need, select the indicator stating "No, it requires changes to meet research needs," and upload a file containing your requested changes.

If the structure you need is not yet defined in the Data Dictionary, you can select "Upload Definition" and attach the necessary materials to request its creation.

When selecting the expected dates for your data, make sure to follow the standard Data Sharing Regimen and choose dates within the date ranges that correspond to your project start and end dates.

Please visit the Completing Your Data Expected Tutorial for more information.

Data Expected List: Data Structures per Research Aims

These data structures are specific to your research aims and should list all data structures in which data will be collected and submitted for this NDA Collection. Please update the Targeted Enrollment number to accurately represent the number of subjects you expect to submit for the entire study.

Data Expected	Targeted Enrollment	Initial Submission	Subjects Submitted	Initial Share	Subjects Shared	Status
Processed MRI Data	320	07/15/2017	0	06/30/2022	0	Approved
Imaging (Structural, fMRI, DTI, PET, microscopy)	320	07/15/2017	370	11/15/2017	370	Approved
EEG	40	07/15/2017	40	06/30/2022	40	Approved

Structure not yet defined

No Status history for this Data Expected has been recorded yet

helpcenter.collection.data-expected-tab

Collection - Data Expected

The Data Expected tab displays the list of all data that NDA expects to receive in association with the Collection as defined by the contributing researcher, as well as the dates for the expected initial upload of the data, and when it is first expected to be shared, or with the research community. Above the primary table of Data Expected, any publications determined to be relevant to the data within the Collection are also displayed - members of the contributing research group can use these to define NDA Studies, connecting those papers to underlying data in NDA.

The tab is used both as a reference for those accessing shared data, providing information on what is expected and when it will be shared, and as the primary tracking mechanism for contributing projects. It is used by both contributing primary researchers, secondary researchers, and NIH Program and Grants Management staff.

Researchers who are starting their project need to update their Data Expected list to include all the Data Structures they are collecting under their grant and set their initial submission and sharing schedule according to the NDA Data Sharing Regimen.

To add existing Data Structures from the Data Dictionary, to request new Data Structure that are not in the Dictionary, or to request changes to existing Data Structures, click "+New Data Expected".

For step-by-step instructions on how to add existing Data Structures, request changes to an existing Structure, or request a new Data Structure, please visit the Completing Your Data Expected Tutorial.

If you are a contributing researcher creating this list for the first time, or making changes to the list as your project progress, please note the following:

Although items you add to the list and changes you make are displayed, they are not committed to the system until you Save the entire page using the "Save" button at the bottom of your screen. Please Save after every change to ensure none of your work is lost.
If you attempt to add a new structure, the title you provide must be unique - if another structure exists with the same name your change will fail.
Adding a new structure to this list is the only way to request the creation of a new Data Dictionary definition.

Frequently Asked Questions

What is an NDA Data Structure?

An NDA Data Structure is comprised of multiple Data Elements to make up an electronic definition of an assessment, measure, questionnaire, etc will have a corresponding Data Structure.
What is the NDA Data Dictionary?

The NDA Data Dictionary is comprised of electronic definitions known as Data Structures.

Glossary

Analyzed Data

Data specific to the primary aims of the research being conducted (e.g. outcome measures, other dependent variables, observations, laboratory results, analyzed images, volumetric data, etc.) including processed images.
Data Item

Items listed on the Data Expected list in the Collection which may be an individual and discrete Data Structure, Data Structure Category, or Data Structure Group.
Data Structure

A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points. Data structures that have been defined in the NDA Data Dictionary are available at https://nda.nih.gov/general-query.html?q=query=data-structure
Data Structure Category

An NDA term describing the affiliation of a Data Structure to a Category, which may be disease/disorder or diagnosis related (Depression, ADHD, Psychosis), specific to data type (MRI, eye tracking, omics), or type of data (physical exam, IQ).
Data Structure Group

A Data Item listed on the Data Expected tab of a Collection that indicates a group of Data Structures (e.g., ADOS or SCID) for which data may be submitted instead of a specific Data Structure identified by version, module, edition, etc. For example, the ADOS Data Structure Category includes every ADOS Data Structure such as ADOS Module 1, ADOS Module 2, ADOS Module 1 - 2nd Edition, etc. The SCID Data Structure Group includes every SCID Data Structure such as SCID Mania, SCID V Mania, SCID PTSD, SCID-V Diagnosis, and more.
Evaluated Data

A new Data Structure category, Evaluated Data is analyzed data resulting from the use of computational pipelines in the Cloud and can be uploaded directly back to a miNDAR database. Evaluated Data is expected to be listed as a Data Item in the Collection's Data Expected Tab.
Imaging Data

Imaging+ is an NDA term which encompasses all imaging related data including, but not limited to, images (DTI, MRI, PET, Structural, Spectroscopy, etc.) as well as neurosignal data (EEG, fMRI, MEG, EGG, eye tracking, etc.) and Evaluated Data.
Initial Share Date

Initial Submission and Initial Share dates should be populated according to the NDA Data Sharing Terms and Conditions. Any modifications to these will go through the approval processes outlined above. Data will be shared with authorized users upon publication (via an NDA Study) or 1-2 years after the grant end date specified on the first Notice of Award, as defined in the applicable Data Sharing Terms and Conditions.
Initial Submission Date

Initial Submission and Initial Share dates should be populated according to these NDA Data Sharing Terms and Conditions. Any modifications to these will go through the approval processes outlined above. Data for all subjects is not expected on the Initial Submission Date and modifications may be made as necessary based on the project's conduct.
Research Subject and Pedigree

An NDA created Data Structure used to convey basic information about the subject such as demographics, pedigree (links family GUIDs), diagnosis/phenotype, and sample location that are critical to allow for easier querying of shared data.
Submission Cycle

The NDA has two Submission Cycles per year - January 15 and July 15.
Submission Exemption

An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.

Contact NDA Help Desk

Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:

Associated Studies

Studies that have been defined using data from a Collection are important criteria to determine the value of data shared. The number of subjects column displays the counts from this Collection that are included in a Study, out of the total number of subjects in that study. The Data Use column represents whether or not the study is a primary analysis of the data or a secondary analysis. State indicates whether the study is private or shared with the research community.

Study NameFilter by Study Name	DOIFilter by DOI	AbstractFilter by Abstract	Collection/Study SubjectsFilter by Collection/Study Subjects	Data UsageFilter by Data Usage	StateFilter by State
The role of sleep in modulating representational change	10.15154/1520667	The aim of this study is to test the NMPH model using a new intervention – sleep – in a statistical learning paradigm that has previously been shown to drive representational change (Kim et al., 2017). In this paradigm, a small number of exposures to an item in a particular temporal context (e.g., B follows A) creates an expectation that when A appears, B should follow. If this expectation is violated (A is followed by X), the memory for mispredicted item B is activated, causing it to weaken (i.e., be forgotten; Kim et al., 2014). However, if the mispredicted item is later restudied, this interleaved sequence of trials leads to differentiation of the A and B memory representations in the hippocampus compared to a nonviolation control condition (Kim et al., 2017). This study also used fMRI pattern similarity analysis to show that moderate (vs. weak) activation of B during violation trials led to more differentiation, in keeping with the NMPH. Interestingly, this differentiation effect was not immediately apparent, and was only observed when learning and the post-learning measurement of representational change occurred on separate days with an intervening night of sleep (unpublished observation). The key question we aim to address in this study is whether interleaved learning during wake is sufficient to cause differentiation, or whether sleep (in particular, REM sleep) is necessary to bring about these representational changes.	69/69	Primary Analysis	Shared
Stat Learning	10.15154/1504119	Participants viewed centrally presented images, one at a time. There were 16 different images presented and each were composed of 8 image pairs. The pairs varied in visual similarity, from completely dissimilar to almost exactly the same. Participants were asked to identify when this square was present by pressing a key with their right index finger. Images were presented for 1 second each, and the ITI was 2, 4 or 6 s. Participants completed 8 runs of this task in the scanner, each of which was just over 5 minutes in length. in the first and last run, the images were presented in completely random order. In the remaining six runs, the images were presented as AB pairs, such that when the first member of a pair was presented, it was always followed by the second member of the pair.	41/41	Primary Analysis	Shared
Reductions in Retrieval Competition Predict the Benefit of Repeated Testing	10.15154/1466212	Repeated testing leads to improved long-term memory retention compared to repeated study, but the mechanism underlying this improvement remains controversial. In this work, we test the hypothesis that retrieval practice benefits subsequent recall by reducing competition from related memories. This hypothesis implies that the degree of reduction in competition between retrieval practice attempts should predict subsequent memory for practiced items. To test this prediction, we collected electroencephalography (EEG) data across two sessions. In the first session, participants practiced selectively retrieving exemplars from superordinate semantic categories (high competition), as well as retrieving the names of the superordinate categories from exemplars (low competition). In the second session, participants repeatedly studied and were tested on Swahili-English vocabulary. One week after session two, participants were again tested on the vocabulary. We trained a within-subject classifier on the data from session one to distinguish high and low competition states. We then used this classifier to measure the change in competition across multiple successful retrieval practice attempts in the second session. The degree to which competition decreased for a given vocabulary word predicted whether it was subsequently remembered in the third session. These results are consistent with the hypothesis that repeated testing improves retention by reducing competition.	40/40	Primary Analysis	Shared
Learning not to remember: How predicting the future impairs encoding of the present	10.15154/1518488	Memory is typically thought of as enabling reminiscence about past experiences. However, memory also informs and guides processing of future experiences. These two functions of memory are inherently incompatible: remembering specific experiences from the past requires storing idiosyncratic properties that define particular moments in space and time, but by definition such properties will not be shared with similar situations in the future and thus are not useful for prediction. We discovered that, when faced with this conflict, the brain prioritizes prediction over encoding. Behavioral tests of recognition and source recall showed that items allowing for prediction of what will appear next based on learned regularities were less likely to be encoded into memory. Brain imaging revealed that the hippocampus was responsible for this interference between statistical learning and episodic memory. The more that the hippocampus predicted the category of an upcoming item, the worse the current item was encoded. This competition may serve an adaptive purpose, focusing encoding on experiences for which we do not yet have a predictive model.	36/36	Primary Analysis	Shared
Neural overlap in item representations across episodes impairs context memory	10.5072/1357077	We frequently encounter the same item in different contexts, and when that happens, memories of earlier encounters can get reactivated in the brain. Here we examined how these existing memories are changed as a result of such reactivation. We hypothesized that when an item’s initial and subsequent neural representations overlap, this allows the initial item to become associated with novel contextual information, interfering with later retrieval of the initial context. That is, we predicted a negative relationship between representational similarity across repeated experiences of an item and subsequent source memory for the initial context. We tested this hypothesis in an fMRI study, in which objects were presented multiple times during different tasks. We measured the similarity of the neural patterns in lateral occipital cortex that were elicited by the first and second presentations of objects, and related this neural overlap score to source memory in a subsequent test. Consistent with our hypothesis, greater item-specific pattern similarity was linked to worse source memory for the initial task. Our findings suggest that the influence of novel experiences on an existing context memory depends on how reliably a shared component (i.e., same item) is represented across these episodes.	32/32	Primary Analysis	Shared
Violation Differentiation	10.15154/1356633	When an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al., 2014). Here, we explore what happens when such previously mispredicted items are later reencountered. According to prior neural network simulations, this sequence of events-misprediction and subsequent restudy-should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based). Specifically, misprediction weakens connections in the representation to features shared with the previous context and restudy allows new features to be incorporated into the representation that are not shared with the previous context. This cycle of misprediction and restudy should have the net effect of moving the item's neural representation away from the neural representation of the previous context. We tested this hypothesis using human fMRI by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions. In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared with items from a control condition that was identical except without misprediction. We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis. Therefore, the consequences of prediction error go beyond memory weakening. If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening. SIGNIFICANCE STATEMENT Competition between overlapping memories leads to weakening of nontarget memories over time, making it easier to access target memories. However, a nontarget memory in one context might become a target memory in another context. How do such memories get restrengthened without increasing competition again? Computational models suggest that the brain handles this by reducing neural connections to the previous context and adding connections to new features that were not part of the previous context. The result is neural differentiation away from the previous context. Here, we provide support for this theory, using fMRI to track neural representations of individual memories in the hippocampus and how they change based on learning.	32/32	Primary Analysis	Shared
Relating Visual Production and Recognition of Objects in Human Visual Cortex	10.15154/1503350	Drawing is a powerful tool that can be used to convey rich perceptual information about objects in the world. What are the neural mechanisms that enable us to produce a recognizable drawing of an object, and how does this visual production experience influence how this object is represented in the brain? Here we evaluate the hypothesis that producing and recognizing an object recruit a shared neural representation, such that repeatedly drawing the object can enhance its perceptual discriminability in the brain. We scanned human participants (N = 31; 11 male) using fMRI across three phases of a training study: during training, participants repeatedly drew two objects in an alternating sequence on an MR-compatible tablet; before and after training, they viewed these and two other control objects, allowing us to measure the neural representation of each object in visual cortex. We found that: (1) stimulus-evoked representations of objects in visual cortex are recruited during visually cued production of drawings of these objects, even throughout the period when the object cue is no longer present; (2) the object currently being drawn is prioritized in visual cortex during drawing production, while other repeatedly drawn objects are suppressed; and (3) patterns of connectivity between regions in occipital and parietal cortex supported enhanced decoding of the currently drawn object across the training phase, suggesting a potential neural substrate for learning how to transform perceptual representations into representational actions. Together, our study provides novel insight into the functional relationship between visual production and recognition in the brain. SIGNIFICANCE STATEMENT: Humans can produce simple line drawings that capture rich information about their perceptual experiences. However, the mechanisms that support this behavior are not well understood. Here we investigate how regions in visual cortex participate in the recognition of an object and the production of a drawing of it. We find that these regions carry diagnostic information about an object in a similar format both during recognition and production, and that practice drawing an object enhances transmission of information about it to downstream regions. Together, our study provides novel insight into the functional relationship between visual production and recognition in the brain.	31/31	Primary Analysis	Shared
Associative prediction of visual shape in the hippocampus.	10.15154/1469793	The way we perceive the world is to a great extent determined by our prior knowledge. Despite this intimate link between perception and memory, these two aspects of cognition have mostly been studied in isolation. Here we investigate their interaction by asking how memory systems that encode and retrieve associations can inform perception. We find that upon hearing a familiar auditory cue, the hippocampus represents visual information that had previously co-occurred with the cue, even when this expectation differs from what is currently visible. Furthermore, the strength of this hippocampal expectation correlates with facilitation of perceptual processing in visual cortex. These findings help bridge the gap between memory and sensory systems in the human brain.	24/24	Primary Analysis	Shared
Content-based Dissociation of Hippocampal Involvement in Prediction	10.15154/1517668	Recent work suggests that a key function of the hippocampus is to predict the future. This is thought to depend on its ability to bind inputs over time and space and to retrieve upcoming or missing inputs based on partial cues. In line with this, previous research has revealed prediction-related signals in the hippocampus for complex visual objects, such as fractals and abstract shapes. Implicit in such accounts is that these computations in the hippocampus reflect domain-general processes that apply across different types and modalities of stimuli. An alternative is that the hippocampus plays a more domain-specific role in predictive processing, with the type of stimuli being predicted determining its involvement. To investigate this, we compared hippocampal responses to auditory cues predicting abstract shapes (Experiment 1) versus oriented gratings (Experiment 2). We measured brain activity in male and female human participants using high-resolution fMRI, in combination with inverted encoding models to reconstruct shape and orientation information. Our results revealed that expectations about shape and orientation evoked distinct representations in the hippocampus. For complex shapes, the hippocampus represented which shape was expected, potentially serving as a source of top–down predictions. In contrast, for simple gratings, the hippocampus represented only unexpected orientations, more reminiscent of a prediction error. We discuss several potential explanations for this content-based dissociation in hippocampal function, concluding that the computational role of the hippocampus in predictive processing may depend on the nature and complexity of stimuli.	24/24	Primary Analysis	Shared
Hippocampal-neocortical interactions sharpen over time for predictive actions.	10.15154/1504543	When an action is familiar, we are able to anticipate how it will change the state of the world. These expectations can result from retrieval of action-outcome associations in the hippocampus and the reinstatement of anticipated outcomes in visual cortex. How does this role for the hippocampus in action-based prediction change over time? We use high-resolution fMRI and a dual-training behavioral paradigm to examine how the hippocampus interacts with visual cortex during predictive and nonpredictive actions learned either three days earlier or immediately before the scan. Just-learned associations led to comparable background connectivity between the hippocampus and V1/V2, regardless of whether actions predicted outcomes. However, three-day-old associations led to stronger background connectivity and greater differentiation between neural patterns for predictive vs. nonpredictive actions. Hippocampal prediction may initially reflect indiscriminate binding of co-occurring events, with action information pruning weaker associations and leading to more selective and accurate predictions over time.	24/24	Primary Analysis	Shared
Human hippocampal replay during rest prioritizes weakly-learned information and predicts memory performance	10.15154/1463093	There is now extensive evidence that the hippocampus replays experiences during quiet rest periods, and that this replay benefits subsequent memory. A critical open question is how memories are prioritized for replay during these offline periods. We addressed this question in an experiment in which participants learned the features of 15 objects and then underwent fMRI scanning to track item-level replay in the hippocampus using pattern analysis during a rest period. Objects that were remembered less well were replayed more during the subsequent rest period, suggesting a prioritization process in which weaker memories—memories most vulnerable to forgetting—are selected for wake replay. Participants came back for a second session, either after a night of sleep or a day awake, and underwent another scanned rest period followed by a second memory test. In the second session, more hippocampal replay of a satellite during the rest period predicted better subsequent memory for that satellite. Only in the group with intervening sleep did rest replay predict improvement from the first to second session. Our results provide the first evidence that replay of individual memories occurs during rest in the human hippocampus and that this replay prioritizes weakly learned information, predicts subsequent memory performance, and relates to memory improvement across a delay with sleep.	24/24	Primary Analysis	Shared
Sculpting New Visual Concepts into the Human Brain	10.15154/1520878	Learning requires changing the brain. This typically occurs through experience, study, or instruction. We report a new way of acquiring conceptual knowledge by directly sculpting activity patterns in the human brain. We used a non-invasive technique (closed-loop real-time functional magnetic resonance imaging) to create novel categories of visual objects in the brain. After training, participants exhibited behavioral and neural biases for the sculpted, but not control categories. The ability to sculpt new conceptual distinctions in the human brain, applied here to perception, has broad relevance to other domains of cognition such as decision-making, memory, and motor control. As such, the work opens up new frontiers in brain-machine interface design, neuroprosthetics, and neurorehabilitation.	10/10	Primary Analysis	Shared

* Data not on individual level

helpcenter.collection.associated-studies-tab

Collection - Associated Studies

Clicking on the Study Title will open the study details in a new internet browser tab. The Abstract is available for viewing, providing the background explanation of the study, as provided by the Collection Owner.

Primary v. Secondary Analysis: The Data Usage column will have one of these two choices. An associated study that is listed as being used for Primary Analysis indicates at least some and potentially all of the data used was originally collected by the creator of the NDA Study. Secondary Analysis indicates the Study owner was not involved in the collection of data, and may be used as supporting data.

Private v. Shared State: Studies that remain private indicate the associated study is only available to users who are able to access the collection. A shared study is accessible to the general public.

Frequently Asked Questions

How do I associate a study to my collection?

Studies are associated to the Collection automatically when the data is defined in the Study.

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Associated Studies Tab

A tab in a Collection that lists the NDA Studies that have been created using data from that Collection including both Primary and Secondary Analysis NDA Studies.

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